Instruction multi-constraint molecular generation using a teacher-student large language model
TSMMG presents an effective model for multi-constraint molecular generation using natural language. This framework is not only applicable to drug discovery but also serves as a reference for other related fields.
Authors
- Houtim Lai
- Yuansheng Liu (ORCID: https://orcid.org/0000-0002-7680-3155)
- Siqi Sun (ORCID: https://orcid.org/0000-0001-7240-8724)
- Xiangxiang Zeng (ORCID: https://orcid.org/0000-0001-6201-0114)
- Leyi Wei (ORCID: https://orcid.org/0000-0003-1444-190X)
- Jianxin Lin (ORCID: https://orcid.org/0000-0003-0359-8821)
- Xibao Cai (ORCID: https://orcid.org/0009-0002-2656-6566)
- Peng Zhou (ORCID: https://orcid.org/0009-0000-5536-1506)
- Longyue Wang (ORCID: https://orcid.org/0000-0002-9062-6183)
- Chunyan Li (ORCID: https://orcid.org/0000-0003-3014-3363)
- Wei Liu (ORCID: https://orcid.org/0000-0002-7908-8476)
- Zixu Wang (ORCID: https://orcid.org/0000-0002-6809-4088)
- Yiping Liu (ORCID: https://orcid.org/0000-0001-7340-2551)
- Jianmin Wang
Institutions
- Yunnan Normal University (CN)
- University of Tsukuba (JP)
- Hunan University (CN)
- Xiamen University (CN)
- Yonsei University (KR)
- Tencent (China) (CN)
- Fudan University (CN)
- Shanghai Artificial Intelligence Laboratory (CN)
- Macao Polytechnic University (MO)
Publication Details
- Journal
- BMC Biology
- Published
- 2025-04-23
- DOI
- https://doi.org/10.1186/s12915-025-02200-3
- Citations
- 6
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 7.52
Funders
- National Natural Science Foundation of China
- National Science and Technology Major Project